library(tidyr)
library(ggplot2)
library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
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## filter, lag
## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, union
library(corrplot)
## corrplot 0.92 loaded
library(plotly)
##
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
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## last_plot
## The following object is masked from 'package:stats':
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## filter
## The following object is masked from 'package:graphics':
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## layout
Abrindo o arquivo
df_cafe <- read.csv("df_arabica_clean.csv")
Precisamos fazer uma limpeza na planilha de dados
Separando as variaveis numericas
Adicionando um corrplot (meio malfeito ainda)
Corrigindo nomes
df_medias$Country.of.Origin[18] <- "Tanzania"
df_medias$Country.of.Origin[21] <- "USA"
names(df_medias)[1] <- "region"
ggplotly(df_medias %>%
ggplot() +
geom_col(aes(x = reorder(region, -media_Aroma), y = media_Aroma), fill = "#753D06") +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
scale_y_continuous(breaks = scales::pretty_breaks(n = 10)) +
xlab("Paises") +
ylab("Media do Aroma"),
tooltip = "y")
Fazendo mapa
world_map <- map_data("world")
world_map <- left_join(world_map, df_medias, by = "region")
world_map_1 <- world_map %>% filter(!is.na(world_map$media_Aroma))
ggplot(world_map , aes( x = long, y = lat, group=group)) +
geom_polygon(aes(fill = media_Aroma), color = "black") +
scale_fill_gradient(name = "Media Aroma", low = "#DE9B58", high = "#472401", na.value = "grey50")

ggplotly()